Dipartimento di Neuroscienze, Sezione di Fisica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy.
Dipartimento di Neuroscienze, Sezione di Fisica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Jan 15;305:123346. doi: 10.1016/j.saa.2023.123346. Epub 2023 Sep 4.
Extracellular vesicles (EVs) are lipid vesicles secreted by all cells into the extracellular space and act as nanosized biological messengers among cells. They carry a specific molecular cargo, composed of lipids, proteins, nucleic acids, and carbohydrates, which reflects the state of their parent cells. Due to their remarkable structural and compositional heterogeneity, characterizing EVs, particularly from a biochemical perspective, presents complex challenges. In this context, mid-infrared (IR) spectroscopy is emerging as a valuable tool, providing researchers with a comprehensive and label-free spectral fingerprint of EVs in terms of their specific molecular content. This review aims to provide an up-to-date critical overview of the major advancements in mid-IR spectroscopy of extracellular vesicles, encompassing both fundamental and applied research achievements. We also systematically emphasize the new possibilities offered by the integration of emerging cutting-edge IR technologies, such as tip-enhanced and surface-enhanced spectroscopy approaches, along with the growing use of machine learning for data analysis and spectral interpretation. Additionally, to assist researchers in navigating this intricate subject, our manuscript includes a wide and detailed collection of the spectral peaks that have been assigned to EV molecular constituents up to now in the literature.
细胞外囊泡（EVs）是所有细胞分泌到细胞外空间的脂质囊泡，作为细胞间纳米大小的生物信使发挥作用。它们携带特定的分子货物，由脂质、蛋白质、核酸和碳水化合物组成，反映了其亲代细胞的状态。由于其显著的结构和组成异质性，EVs 的特征，特别是从生化角度来看，具有复杂的挑战。在这种情况下，中红外（IR）光谱学正在成为一种有价值的工具，为研究人员提供了 EV 特定分子含量的全面、无标记的光谱指纹。本综述旨在提供关于细胞外囊泡中红外光谱学的主要进展的最新批判性概述，包括基础和应用研究成果。我们还系统地强调了新兴尖端 IR 技术（如尖端增强和表面增强光谱方法）的整合以及机器学习在数据分析和光谱解释中的应用所带来的新可能性。此外，为了帮助研究人员在这个复杂的主题中进行导航，我们的论文包括迄今为止文献中分配给 EV 分子成分的广泛而详细的光谱峰集合。
